{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 08: Algorithms 04. Total variation minimization algorithms. \n",
    "\n",
    "This demo presents the total variation algorithms in TIGRE. Total variation algorithms try to minimize the variation (gradient) of the image, assuming its peicewise smooth, as most thigns in nature are (i.e human body)\n",
    "\n",
    "This set of algorithms perform especially well when the noise is very significant or the number of projections is small, however, they require more computational time and memory than the other algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIGRE parameters\n",
      "-----\n",
      "Geometry parameters\n",
      "Distance from source to detector (DSD) = 1536 mm\n",
      "Distance from source to origin (DSO)= 1000 mm\n",
      "-----\n",
      "Detector parameters\n",
      "Number of pixels (nDetector) = [128 128]\n",
      "Size of each pixel (dDetector) = [3.2 3.2] mm\n",
      "Total size of the detector (sDetector) = [409.6 409.6] mm\n",
      "-----\n",
      "Image parameters\n",
      "Number of voxels (nVoxel) = [64 64 64]\n",
      "Total size of the image (sVoxel) = [256 256 256] mm\n",
      "Size of each voxel (dVoxel) = [4. 4. 4.] mm\n",
      "-----\n",
      "Offset correction parameters\n",
      "Offset of image from origin (offOrigin) = [0 0 0] mm\n",
      "Offset of detector (offDetector) = [0 0] mm\n",
      "-----\n",
      "Auxillary parameters\n",
      "Samples per pixel of forward projection (accuracy) = 0.5\n",
      "-----\n",
      "Rotation of the Detector (rotDetector) = [0 0 0] rad\n"
     ]
    }
   ],
   "source": [
    "# Define geometry \n",
    "import tigre\n",
    "import numpy as np\n",
    "geo = tigre.geometry(mode='cone',default=True,high_quality = False)\n",
    "print(geo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and generate projections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tigre.demos.Test_data import data_loader\n",
    "angles = np.linspace(0,2*np.pi,100)\n",
    "head = data_loader.load_head_phantom(geo.nVoxel)\n",
    "proj = tigre.Ax(head,geo,angles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets create a OS-SART test for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OSSART algorithm in progress.\n",
      "Esitmated time until completetion (s): 21.39153\n"
     ]
    }
   ],
   "source": [
    "import tigre.algorithms as algs\n",
    "imgOSSART = algs.ossart(proj,geo,angles,20,blocksize=10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Variation algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POCS Algorithm in progress.\n",
      "Esitmated time until completetion (s): 195.110544\n",
      "\n",
      "     Stop criteria met: \n",
      "     c = -0.99762905\n",
      "     beta = 0.809727868221\n",
      "     iter = 21\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/reuben/TIGRE/Python/tigre/utilities/order_subsets.py:57: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return block_alpha[new_order],index_alpha[[new_order]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POCS Algorithm in progress.\n",
      "Esitmated time until completetion (s): 15.445043\n",
      "\n",
      "     Stop criteria met: \n",
      "     c = -0.8667783\n",
      "     beta = 0.809727868221\n",
      "     iter = 21\n",
      "\n",
      "OSSART_TV algorithm in progress.\n",
      "Esitmated time until completetion (s): 14.590917\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#  Total Variation algorithms\n",
    "# \n",
    "#   ASD-POCS: Adaptative Steeppest Descent-Projection On Convex Subsets\n",
    "#   Often called POCS-TV\n",
    "#  ==========================================================================\n",
    "#  ==========================================================================\n",
    "#    ASD-POCS minimizes At-B and the TV norm separately in each iteration,\n",
    "#    i.e. reconstructs the image first and then reduces the TV norm, every\n",
    "#    iteration. As the other algorithms the mandatory inputs are projections,\n",
    "#    geometry, angles and maximum iterations.\n",
    "#  \n",
    "#   ASD-POCS has a veriety of optional arguments, and some of them are crucial\n",
    "#   to determine the behaviour of the algorithm. The advantage of ASD-POCS is\n",
    "#   the power to create good images from bad data, but it needs a lot of\n",
    "#   tunning. \n",
    "#  \n",
    "#    Optional parameters that are very relevant:\n",
    "#   ----------------------------------------------\n",
    "#      'maxl2err'    Maximum L2 error to accept an image as valid. This\n",
    "#                    parameter is crucial for the algorithm, determines at\n",
    "#                    what point an image should not be updated further.\n",
    "#                    Default is 20% of the FDK L2 norm.\n",
    "#                    \n",
    "#   its called epsilon in the paper.\n",
    "\n",
    "imgASDPOCS = algs.asd_pocs(proj,geo,angles,niter=20,tviter=25)\n",
    "\n",
    "# For OS_ASD_POCS, one can simply change the blocksize and order strategy for algs.asd_pocs\n",
    "imgOSASDPOCS = algs.asd_pocs(proj,geo,angles,niter=20,tviter=25,blocksize=20,OrderStrategy = 'random')\n",
    "\n",
    "#    SART-TV \n",
    "# ==========================================================================\n",
    "# ==========================================================================      \n",
    "# \n",
    "#    This implementation differs more from the others, as it minimizes the\n",
    "#    ROF model, i.e. when minimizing the total variation of the image, it\n",
    "#    also takes into account the information of the image. If only the total\n",
    "#    variation minimization step was run in the rest of the algorithms, the\n",
    "#    result would be a flat image (as that is the minimum total variation\n",
    "#    image), altertatively, the ROF model enforces the image not to change too\n",
    "#    much.\n",
    "#    \n",
    "#    This algirths performs better with more projections, but noisy data, by\n",
    "#    enforncing the TV very little\n",
    "#   \n",
    "#    The optional parameters are for the total variatiot part of the image:\n",
    "# \n",
    "# \n",
    "#   \n",
    "#    'tviter'       amoutn of iteration in theTV step. Default 50\n",
    "#  \n",
    "#    'tvlambda'     hyperparameter in TV iteration. IT gives the ratio of\n",
    "#                   importance of the image vs the minimum total variation.\n",
    "#                   default is 15. Lower means more TV denoising.\n",
    "imgSARTTV = algs.ossart_tv(proj,geo,angles,niter=20,tviter=100,tvlambda=50,blocksize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UCiEUCqGurm7Sgj1XoD69++67sXz5ctTX16O+vh5tbW1Ip9MIBALcpwAsY0x+LSsQZLehcaXmDZGfRy6XY+MhCT5aKOiPjqfzkdDJZDLIZDJoa2vDwMAAv//CF74Ap9OJZDKJZDLJi2ep+0wVkKRoXHnllbjyyivR2NiYV5hTW+Wd9FR/Uyli00FV7tQ/wOxLsjPJz4ueSyQSQSQSQXNzM5qbm6e1I80WBQt0IYRTCHFQCPGfE+9XCiH2CiHahBDPC7M8nYaGhobGAmEmGvpXARyT3n8TwN8ahrEawBCAh0rVKFq5yD1xzZo1WLNmDfuQy6skbRFVLxZ1Jc1HoRS6gssrqqqRq5ZxQi6Xw5tvvok333wTbrcbnZ2duP7663H99dcjm83i93//90vSX9OBwvXXr1+PpqYmRCIR3n0Q3SJrkzIuBS0dMHdWIyMj6OjoQEdHB3/e0tLCGjrZLeYDt956K2699VaUl5ejpaWFd15jY2NIp9NIJBKsGRuGYbtzkDV2WbsmKtHhcLBGCljpQRrn8veA1d+f3qvzhbTHaDSKI0eOIBaLIRaLYXR0FBs2bEAwGEQwGMRv/dZvlWzHY2cXoz+/3481a9agsbERjY2NvJMgyNq53GdzpZnLkM8xlZZOUacqKI1Da2sr01v5/O5LgYIEuhBiGYDfAvDkxHsB4DcBvDBxyNMAPl2SFknw+XzYunUrwuEwwuEwOjs7kclkJglXmfpQt6aAvQvTbDHdA6BrDQ0NYWhoCC+++CKEELjppptw0003YXR0FJlMhreZcwXyRQ6Hw1ixYgWWLl0K4GI+ldHRUTYu5XPPvJSEend3N7q7uzlgy+/38/35/X40NzczNTaXqK6uRnV1NTZt2gS/34/Ozk50dnYiGo0im83aLvokSGXDJv3Zcd9kFLUz0hE/T+NaNhoSreh0OpHNZvnZ0ncytdPX14e9e/di7969AIBt27ZZjt+8efOs+yofzUD3FolEOLUGOQ3Ic1YW5oUI8VLnm1GpMbs/Uojs4iJCoRDnlmlqasprdC0FCk3O9XcA/heA8MT7agDDhmGQatAJoKEUDRJC4Etf+hIA0//U5/PxYFN5Z5oIqiVbNp7QcYSpfHtngnzWdPm1LAwNw8Bbb72FHTt2AABuvPFG7N+/H9u2mXaODz74YNZtUkHC/OqrrwYANoTGYjHWJigoyO63dq8XGmRs6u3tRTgcRjAYZIPk6Ogoli1bhvb2dgCmAarUi5HD4cDv/u7vYuvWrQBMDey9997jiUwauZ0BU/ZeIQ6cfkOfA5MN9/R78lcHzOAjr9c7KYGUPPZoLqhtkY2lmUyGYw6OHTuG1tZWXHXVVQDM7Ixr1qzB+++/X7Rvup1HC72nBHmtra0cFyHfr+pllm/HDcxP0jCSHaoxmtoDmIFEXq+XPXao/8nw3NDQgFOnTrFH1lTnKgbTSjYhxG8D6DUM40AxFxBCPCyE2C+E2F/M7zU0NDQ0CkMhGvr1AD4lhLgdgA9AGYDHAVQIIVwTWvoyAOftfmwYxhMAngAKc1uMRCLshkbRl7Sa0eo4lT+4zBkC9iv3VCtgIZ4yM/mezjcyMoJAIMDa486dO/HLX/6SqYHW1laOgiwVnE4nIpEIu4PRFrxYP/NLCePj4+jv70cgELBkcRweHkZ1dTUAcydSare7pUuXWvzj5XwhwEXNUvb/VjVun8+HZDI5aWdEY4goE3JdBC7mJiEQv04g7VHOVkmUpKzpym6L1DbaXfT09CASiTDN8u677wIAVqxYweO2UEzFE1O7aFzW1tZajqd7l3cf+ebXfI9fu9gAwKpdU94d+XOiksrLyxGJRNi9VpVns8W0GrphGF83DGOZYRhNAO4F8LphGPcD+AWAuycO+zyAH8+6NQCuvPJKfoAbNmzA3r17Jxl66HuVmyOoLocyV1kI953P+DHVbwrB+fPnMTg4iMHBQQwMDODGG2/k81OgTykgG5tWr17Nbp8ulwvj4+NIJBJzxjfOFyhlsRxWvWzZMoRCIXYR8/l8JaOLyE6zbt06VFdXo6GhAQ0NDTh+/Diy2eykXDUqradScrIx3zCMSWHtdB7ZCKi6McrPTqZx5Pkgc+iUAkP+c7lc/H13dzeGhobYwLdx40YEg0Fs3rzZ4h5ph6nc8NRrAqaAa2pqQlNTE1MvKh8u99dM5+Rcws4wK7eVkspR8QuSVeQ6vHz58kkpSWTMZszOhkx+FMDXhBBtMDn1f5nFuQCYN7J582b2ymhsbMTAwEDe4wt9qHPNAasDdqq/EydO4MSJE+jq6sI111xjefClAgmXYDCIFStWsBdILpdjYb7Qk2K2yOVySCaTFt90v9/P0bh+v7+kPuk0idesWYOysjK+xvj4uO11KBJUNjTTe0qqJfuLy89CthXJAk6NflY1Rfmc9Jms3NB9qDtY+j6ZTKKtrQ39/f3o7+/Hxo0b4Xa7J9mq7JBPybKbew6HAxUVFaxoqIufKjDl80+3+14oqG2lakeq4A8GgygrK2MZB5RWPs2oYpFhGG8AeGPi9SkA15SsJQC+9rWv4bd/+7fZQLhv3z4EAgFb7wupTQV1iLqly/c9na/Q86ptKbQNb731FpqamjhytJQJpsggs2nTJmzcuJH7jzIlfpQ1cxm5XA6jo6O8faVUp3T/Pp8P0Wh0xguX6tnj8Xg4Wvm2225Df38/zpw5A2BytLI8bkijJYGsCi4SvOTBIQtDWhDULT2Brit7xKjtls8DWF165bbKXicjIyNM/UUiEVRUVGBwcJD7dDrjaL65JV/H5/OhqanJErikOhCoAlLFpTB+7Z4p3QdRfel02rKzcTqd7BYKXExXMZ18KhSXfKSohoaGhkZhuKQE+uOPP86FcB0OBz788MMpebtiofLtdi56xWyDpvMplTWVdDqNtrY27NixAzt27EBPTw++8IUvzPiadtcgvjeVSllSFFCOdzs3xY8iKCUA5XNJp9OorKxkOqS1tbUk/uipVAq//vWvuQi4w+GwBDip/alSBmq6CfpM5cflnZMcRKPmLJFBgUVkJJVD0GXtHMjvD07XIGqF+nNgYABr166FEAI333wzbr755mldftV22s0Jih1Qod7vfPHmpbyWusug/OyEXC6HYDDIQVT5aMFiaZhLpki0y+XCI488gnA4zDVCKYveXHDgC+VbTQ88Go3i7NmzPEHC4XBJPDIcDgd7eTQ3N6OsrIwpifnMHzNfoAhSAMxJUp+GQqGieHR1MldVVeHWW28FYOYQd7vdXPl9Kl5X9S9Xz2tnKAUuerRQoBx9Vki7ZfqE+HqiS1QKRgXx/FRE5Ny5czxmaGHMR6nYCXL1PT2Lmpoarlegtp/+2/HnwPxSLYXSrirNJYOKo5Nimsvl4HK5OKJZzdkzW1wyAv2BBx7Apk2bsGHDBn5oDz/8sO1gmW/MlE8vpM2GYeDQoUMcJdra2sqFqmcDykoImHzdCy+8wDwdFTZYTCADFGDaIXw+n8UuUQrD6I033sjFiB999FEcPHiQOXVV4MoabL6gH5XLJiGr2nHIzRS46HIqLw6yayRdT92RqEF1dry8alyl/jx58iRWrFiB5uZmXsDsxvZUY13W0GmBq66uhmEYnGLW4/FY2i3bsWQsBG8+3dy3c2OUj6ddpAy3220pwKNq8bPBJUW5aGhoaGgUj0tGQ+/r68Pq1ast2w/KJzEbXrtY2GkhM9XS7c6p3gsVuF61ahV+8IMfFNtcPqff70ckEgFgaj4dHR2scdXX18+JTWKhQWOGAjpIY0qlUiVJ8+Dz+bhE34EDB5DNZpnGkJNwAWBty45ykH3NyXcduKh9y/lYiHZRy/LJv7fzspA9WlSKjbRz2TNGBnHxhGQyiWg0iurqanR2dgIo3lVYCMGUGOXgp3PF43FUVVVN6bu/kF4t+TyNpjo+n5ZOdgq6H7v4g9lgwTV02pJ9/OMfx549eyzf7dq165Iy4M3WYGJnfCUDlNvtxq233spBMcWePxgMciIgwzA4k14sFsOpU6cuqf4sBeSJrhp8PR4PVzkqlnqprKxEc3Mzent70dvbC7/fj0wmg3379mHfvn1IpVLI5XKcnCmf4JGTY8nZFEnw0n+12AVBFgKAvbGRAukoWIiEB51X7QdVgHo8HksSsVwuh1gsBo/Hg+XLl2P58uVYsWLFjPpPbiP5XlO0qvw3ODg4KQmW3HcfZcj3ItNmACz2jVJgwTV0upG2tjZLQhsAOHv2LG655RZLkYB8mq98rlIhn+Au1k/d7jxy1rx0Oj2rcwphFjwgn/ZgMIjR0VG+j/7+fqxcuXLeikAsBFRBIJcfLBadnZ1cpJrC5MlXm5KrkSCma6nBQXJSJzI+yu/lUH45I2M+gyp9J/PwQggLb0+ZSOkzeRcgt1l9T+ekBUBehArRKO20c/W3U/HjH3UBDtinBJC/o/eLTkPX0NDQ0CgNFlygr1q1CqtWrcKHH36Ir371qwCs28kjR45MCqOdCoVY4GfrNWPnTlUo7KLmfD4fHA4H3nnnHVx99dWc7namEEIgEAjwOSORCJYsWWLxfY7FYotCA8oH0shpF0Wui8XuSurr65FOp7Fz507s3LmTNXXKJ9/R0cH5cRKJhCUXuV2MA7VLbaf8PXAxZaycg4g8X5xOJxdCVwtcEL1CLouUQljeJcjvgYvUD5VapPFDUY1OpxMDAwMYGBgoiHKxmxMOh4Pz1Kv3pHrrlAJz6bc+U9jlfaH7lm1a08WxFIIFp1za2toAANu3b8ef/dmfYd++fZy/5bbbbsPbb7+NlpYWAJO3iCpkGkTd4tgdC+SnadTPSz046HxUcIIy8JHAKPacPT097JcdiURw//33swB/+eWXkUqlLBNosQl3OyNhIc9Ode8jnD59GmvWrMHrr78OAHj++efR29vLOfuPHj2KnTt3WqrRT9cm1R9cCMHPRf6NzJsTJy4bPUkIAyYXa1e9yM43XX3mcvUjh8PBFKfL5UJ5eTmfm85ZDAzD4Bz8QphJquTgIjlj5UzOWewxdnJiLpHPV10eL6Voy4ILdMK+ffuwZcsWnDlzhgfc008/jQcffBDXXXcdAFNAvfrqqxZPgmJWNFXwF3IO9VrqgjETCzidj4yWwMUJ/OSTTxZ8H3bnTiaTHBiybNky5lEB4OMf/ziOHz+OUCjE7U0mk3NS2X2hQCliAdPLpbu7u6DiDPkWtkQigc7OTk71OjQ0hGAwiD/5kz8BAPzDP/wDVq1axQUMjh49ygsq9bHb7bYIbDmlrXxtmcNWPVLyabCyT7sqbGU/duobeREgjXEq/np8fBzl5eUs5P/rv/7Lth3TQRbocqANoaysjDNn5nsW0ylpdsg3L2ciPOfCNkf3SB5opVpYFpxy0dDQ0NAoDS4ZDf26667DmjVr4PV62Uvj5z//OTZu3IjPfvazAIC//Mu/RFVVFa/kY2NjyOVyHOo+MjICl8sFn8+H/v5+AMCFCxewZ88ePPDAAwAmpw/N57Fix3fNJEJOhfp7ek33Oj4+jmuuuQZHjx4t+Jx2cDqdqKysBHAxTJ3uLRAIoLGxEeFwmLUl2oLno7NUaiabzWJoaAjV1dWXRBoB1U9bvg9yMZyN9mMYBlpaWpj283g8iMViXNP0+uuvxxVXXIFjx47xNZ1OJ+LxuKUdyWTSorGPjo7yGD106BB27tzJOwlKlWtHlxDUZ6beI3HU8jjO5zmj2oTk3evY2BgSiQT74Xs8noIiG6fywyf+X/0+H+01G+SjOuT3U42P6cZ4vp2TSp/l+51dYfbZYMEFOj3kz372s3jzzTcRCASYV+7r68Ptt9+Or3zlKwDMgVBTU2Ph4txuNx5//HEAU6f2pAlG2x26Lg38qYwy6gPPF5YsBw3ZuS3ZDRxKo3n8+HFLsEUxcLlcqKur4xB/MmwRQqEQkskkent7LZN6dHSU+4dyYg8NDQEwFxp1cnm9XmzatIlD7EmILZSAl6kMAJy7ZqqFSoVMQ8i/Wb58OdcPBcwxKBsGx8bG8LOf/YxpK3Ibld0Wn3/+eaZh8uHcuXOWdLKU71weU3KgEY0xdcGl8SMvDqrLoUyxyWOV3BrpeBo/p0+f5vMXG6bucDi4bmgwGLRVoGQuX76nVCqF8fFxjI32PUTdAAAgAElEQVSN8bMdGRlBLBab5GpJ99jS0oIlS5awK7QKu5gQ+Tzq51PBzvA9FeRnUmyt1nxYUIEuhOAcEXv37sV1112H8vJy7N69G4BZmurkyZOc96C8vBxbtmzhqDUyqP7FX/wFAGD37t3YtWuX7bUof3VTUxMXfpDPMZWR1O4hTfW+0IGwdOlSzrvyjW98A7fccktBv1MhJz3auXMn71icTidrWIA5kQcGBpBMJnngu1wuRCIR3vUkEglcffXV6OnpAWBGRqoFOGgRIIFeCuv8bED37/P54Ha70dXVBQA84QtZJNWJRcmTvvnNb8Ltdlu0y4MHD/L9njp1CsPDw1i+fDkAYOXKlejv78fIyAgLvy9+8Yvo6OjAv/3bvwGYrH26XC4MDAywIuPxeFBbW4vz589bjK12xc7lBGCydk1avvps1Nzc8nck6KlcWn19PSoqKvDCCy+grq6Of1+IzUXVUCsqKriYNykxcoCVnbFW3p2QjYmetcfjmTTnDMPgc54+fRrV1dV8v/JCWGi7p4P8PGZy3ng8zgtTvh1ksYqd5tA1NDQ0FgkWTEMXQuCP//iP8Xd/93cAzMxufr8f8Xic3ZmEELj22ms5K1tXVxdefPFFjs5bsmQJtm3bhjfeeAMA8Nd//dfYsGED/vZv/3bSCrd//34AZvRpX18fPve5zwEwNR+5cG4pNE27bZsdx1lVVcVh/r/5m7+J/v7+GbtTyelyN2/ejEwmw26fHo+HfZYJoVBoklZAPsKEVCplST/wzjvvsFZGnjTHjx9nLb68vBxbt27l7W2pfYrzgTRU0ihp3JDHSVdX16QshoXA6/Wy3ebw4cPo7+/ne8pmswgGg7xrXLlyJTKZDOfk6evrw7p16xAIBDhDY09PD+6++252Sf33f/93CwWTyWRw4MABy87ijjvu4Hz2BLWurt1Yld0LiT7K5yZH51GjXOl/ZWUlVq5ciQ0bNuDDDz+0nH86yD7uoVAIK1asYEpJtVvZVSSTKUoqyp3NZnm3GQ6HMT4+bmmPTB8lEgm88847vOtZu3ZtQe2eyb3RNeVdkPre5XJN2gnJhblLnYpjWoEuhFgO4HsA6gAYAJ4wDONxIUQVgOcBNAE4A+AzhmEMFXrhBx98EBcuXOBBWl1djbKyMkvgSzwex969ey0pAZYsWcKCpKysDL/4xS+wceNGAMArr7yCFStW2Cb1pwdNi8Ndd90FAHjuuecmpSm1CwKYDnY8O3DxgaVSKXg8HqY2KKCK6KVsNouXX355Rlstt9uNiooKDkQi1zrZgJnNZi2pEwzDgMfj4WMoUEX2ZwYuulNRQiU7gzANSp/Ph/LycgsXOx85Y0iYUzKycDiMoaEhbtfZs2cRj8dn1KdOpxOf+cxn+PkNDw9b8lcThUWLJpVnowWxoqICiUQCgUCA7RIUOEcCnWxAKsjQGgwGsW3bNhw+fJjnRzQahdfrZRqHUgXQ9xSoJBs4iaaQQ/9l98lsNsslCQGTaslmsywEly5dikOHDiEajXJxj0L9uule/X4/F9SW89VP5YJI81H+3OVyIRwO8zmIT5ddHVV6KZ1O8zOQ6RsVxRpg81GNshLl8XgsjgnxeNziWixfe7780DMA/odhGO8KIcIADgghXgPwIIDdhmH8lRDiMQCPwSwcnReRSAQf+9jHAJga6U9/+lPWplwuF86dO8dFfoGLD5YGJNWPpM4qLy9HNpvlQhi5XA733Xef5ZqqJkMPlPxpt27dimPHjlky3wGwDKh82tB0D0DW/G+44QZcuHCBtZTGxkasWrUK3/ve9wCYwmimD/SWW25BTU0N85uUHEpeRADrZJOrwgD22pHD4eB+WLp0KSorK3kRlSFrIUJcLIJA0YmlDlqixFGA2V+BQAB1dXU8XrLZLE6dOsVCb2xsrOA+JS+oQCCAjRs3cl1b4ozPnTsHAJz8itpB0bfkrdTT04NAIIBwOMz9c+HCBQQCAV5wp9Nyc7kczpw5g8bGRst10+n0pIyMJNBUAzhgCjS5QDQt3Op4IA+esbExVFRUoKGhAYC5sLS3tyMej/MCnw9qP1911VUAzOcUDoctBt58nLHqdaOCnCAAU5lraWnhsR6NRi3jzeFwwOPxWOSLvJDISttMguzsFgTZqYKUDNrB+Xw+CCH43GNjY0in07yoT3fPM8W0At0wjC4AXROvR4UQxwA0ALgDwCcmDnsaZvHoKQX60NAQNmzYAMBMePTJT34S7733HgCgvb0dDocDPp/PUmA1m83yqlxdXY1oNMraZCwWY7oBMAf5M888g5/+9Kf4zne+AwCs9VKHlZWVIRgMYtOmTfybI0eOTJlWVtZ26Df53MTk3zidTrbs7969G4ZhcITh2NgYBgcHLQOuGNTU1PA1qHoPaajqAM1Xzkw+Tg1kqa6uxpe+9CUWQv/xH/+BtrY25HI5nvgtLS1ctAAwPRCmqoxTLOQQfgqSkaMNaWdQDG12+PBhAObOzeVyseJx6tQpHD16lO+fNHASpA6HA+Xl5UyvDA8PM61FWiotsn19fQCA73//+zhw4AD+5m/+xtKG+vp6/r98+XKUl5fj/PnzAC5qz+rWneD1epHL5SxeKCRI5OhSeaF1uVwIhUI4dOgQAFP4XHXVVTwXaFdJRsyZgOasPK+mE1qqW6UKlcoIBoO46aab+N7a29t5Bw6YCh8totNVj5oJ7OgVuV1Es8hQC5HIu+ZSYkZSRAjRBGALgL0A6iaEPQB0w6Rk7H7zMICHgfnjVjU0NDQuRxQs0IUQIQA/BPDfDcOIKtqoIYSwXWoMw3gCwBMA4PF4DHITPHz4MNavX8+c7YEDB3D11VcjkUhMuSUNhUK8zUqn05YcED6fD4FAAG+99Rbuv/9+AMDNN9+MzZs344/+6I8AAM8++ywOHjzImk8ymZyUA0PdgpFWSzsH1ciRTyOsra1lF0un04mbbrqJg0kikQjeeOONWeVDTiaT6OnpYRco4san0nCmg9y3gMmlx+Nx3kl88YtfhNfrxcjICLvqRaNR9Pf3My+o9icw8wIhKuSEVICp+RFNReft7+9HMplk3nQmPD712fHjx7Fv3z5Lub6uri6mtdLptMVtz+FwIJlMcrsqKiq4jqQcNBIIBPiYw4cPY8OGDZzmoba2Fr/61a/wqU99CoBZ9ISKUBPVQb7LdA5Vm43H41zggz4nikU2UFLxaWprd3c3a68bNmxAMpnkc7z//vvcjzPVJOXdLKVjmEmsRz7kG0NOpxNr167lmAEhzNoAZHwmm8dcQIiL9VJVwzKxA3IRluHhYcvYsLNPFYuCBLoQwg1TmD9nGMaLEx/3CCHqDcPoEkLUA+jNf4aLoK3Yrbfeip6eHp74v/Ebv8HRZLRVdbvdiEajlsAIuaoKbcHlQW4YBs6cOcNb4kgkggMHDuCaa64BAHz7299GT0/PJM4431ZWhprPeSrBuXHjRha0gJl8bGhoiAN24vE4jh49yv3x1ltvTd95CuRMegAs0YjUDpW7pACVfG1XF7Px8XGMjo7ywkl5SRKJBNMUKo0znc2hGMFOvuD0DLxeLzKZDPr7+1mAd3R0YHh4mGMO5GjN6UD363a7sWbNGubEE4kEGhsbWch5vV6Ew2EeI/F43KJUxONxiwAFLgZekXBub2/H+Pg4R/R2dnYiGo3ihRdeAGAKH3push91Op22cPdygJwqSIQQLLxVSo08mBKJBMrKytg/PJ1Oo6uri8956tQpGIbBtOhMIPvP0/iQDfHA7OsYpNNpizLjcDh43tMiVIi9YqaQ260KX7qenG8qk8kwDdrX14fOzk6L15j8f7YoxMtFAPgXAMcMw/i/0lcvA/g8gL+a+P/j6c6VTqdZI43FYhZNVxaiNEHC4TBCoRC/p2IN5NXQ0tKCzs5OnrikGSUSCdaoOjo6kE6neUUs5AEWqk2qq7D8m2g0CsMwcMcddwAwOfP9+/czx0fukjTpaecyE0SjUcRiMTZGqgOMFkhZSFM7ZW1JjqZTeXbDMCycMaX6tdN+VRtCvj4sZiKTgCNhk8lk0NHRgXg8zpOIJrjcx4WCdhdksKI2ki2AJqAQAqOjo9znampev9+PcDiMqqoq3o3RAihP9mg0aol4ps/omiS85YnvdDotpcyAi95I5EAgc9bUZ3IUKwB+lm63G5s2beJ2ffjhh7yQAebilU6nOfhvJqBryGNQFuRTPXvqc3lxyifwZMVDPq/d+VX3wmJAAV30muwUNNZcLhdisdikHQoJ9MHBQa7OpGK+vFyuB/AAgMNCCFqq/z+YgvwHQoiHAJwF8JlZt0ZDQ0NDo2gU4uXySwD5lrObZnrB7373uwCAZ555BsePH+ekRuFwGJlMBl6vl2kJsuqTZhEMBpFMJnk1PHnypKUoMG1Bt2/fjq9//et8zebmZvbVlusZAvYh/IWs3nZctazVtre3w+Vy4dSpUwBMbdrtdrM7E7mPvf322wX1mx1isZiF3yW/YnnXQ/Ul5TaqmoDqoilvj8kt78CBAwBMzTgcDqO5uZk9jFQfaNU9bTpN3Q7qbxKJBJLJpCVPixovkEql0NXVxcfMROOhXd7+/fvx0EMPsUbV3t4Oj8fD5woEAux6RpC/Jz/vRCJh2QXJvsnBYBBPPvkka8Zr167F2rVrmYIhTVvVUgFYKBeZglK37slkkjVdmZYxDIPtR4FAAGfPnuWdgtPpRDgctni5HDp0aMb5WxwOB+82ZJpIpixVmlO+R7txpGrpNEap7X19fRZ6pbKyEhUVFZO8tkrh3WLnNUTnpaA92caRyWR4fPX09Exypy2lp8u8R4qSMH7mmWfg8/nw0ksv8XePPvoodu7cyQOut7cX586d48FRVlYGr9fLUXmAuYUh3+7+/n7U1tbi5Zdftlyzvb0d7e3tAExO/bbbbrN8X8hWTN0mTuX+R8fJk56KFauUyGyyK/b19WFoaIgHcjAYxMDAANsgNm/ejHXr1lnuIRqNYnh4mLfqyWTSIvCJd6UtcyaTQVdX1yQ6IJlMoqmpCQDQ0NAwKbeGDLsBm88nWe0fwlQ5suk3sVgMHR0dRSU8IsHQ1taGXbt2sW3jP//zPxEOh/GHf/iHAICPfexjGB8fZ7/88+fPW1xps9ksRkdHLcZZh8OBkZER9pfv6uqytPH48eM4fvw41q9fD8A0Tnq93kn2EDl5G7nSyoZ7OwpTDiKjBYIEbDweRzabZddP2WWY7uXIkSMz6keiIGgO+3w+JBIJVFVVMbUXCARQVVXFtJUceANYqVM6Zz6hni+f+NDQEBwOBy+SU/m2Fwp1oZmKNpH/p9NpprL6+/ttk/DNG4deapDfaDabRXV1NR5++GEAFyMP/+mf/gm/8zu/A8D0sZa9WjKZDOrr63HzzTcDAN59910kEgnWpsiSfc899+CTn/yk7fUbGhqQSCR4cuUT5G63e1IVGtmH1U6TzyeMCPIiIITA008/PVVXTYtUKsVlvQBzsVq6dKlFIzt16hQnIqMwbNmvuKyszJLBkrx+SKDHYjGcP3+e+1hOTyBHIM4UpbRlkAZ57NgxjIyMFDU56Dfbtm2D2+1mH/tHHnkEhmEwx/7888/j+uuvZyG4Zs0ay/Wqq6uxZs0anDt3zpLwTDaQj4yM4MiRI5bPvF4vj8l0Os0BKfJ4kflxMgrLXH8+jVc20spaPxma1QIb1K6f/OQnRRc/kbl+sl/JhmV1/sjPmqJCybNKjhSn81IGRjklA/W3fK/y+eU+sVsgSgVZychms0gmk5zWgVJ+q+0qFbRjuIaGhsYiwbxr6K+99hoAU1Om7RA3xuVCa2srb2cvXLhgifwqKyvDgQMH8OKLpuekasUHzBX4scce451Ad3e3Zdt46NAhtLa2WhISUe4NOifx43ReWasBJmuOhVjOZV9UOna2q3Mmk8Hw8DB7U1C0ouqCSRGKxOfJHHk8HkcqlbJ4U8jtyuVy6OnpsfDl5G5K28i6ujq43e68uadVlEorobZS2wcHB4vOL03PZu/evWhubmYaj2wupF02NjYiGo0yhTc4OAghBI+fgYEB5tGJ9kilUkxVAWbh802bNrHm+s4773DCMwBYsWKFZewBF7VbNQGaTPOp/v8ul2uS54s8NsilUC62IH+fL+fMVCAqiNphGAbGx8ctHkqqT3q+uUDPhHLyyxGWmUwG6XSar+P3+yd5I01VX8DO5jOTe5zue5nmkuNl5rrc44JlW+zu7mYDE2B26pIlS7By5UouyKtum2jyqOHExHcCZgfeeeed/Lv77rsPDz30kOXaS5cu5e1ZIBDA9u3bOfnS7t270dfXN6UhaDauT7N1m5JBAk1N9CPnByc3OwDsOqcadO0MNPJgX7JkiWVrf+TIEQwPD3O/h0IhuN1upgyqqqqQSqU4qIP6cjZUyFT0lkp1FXMduT+Gh4f5elT0gQzPNTU1+OCDD3j8kBFa9tMXQrDLH2DaNuSxfO2118LtdrMNYnBwEGfPnmXqrLKyEi6XCxUVFWhtbeX7OnbsGCs4aug4URiqj7eav0TuS7v/JJABWIpdENQ5qfa3mtucqFT5vGp/04JHIIEoU0H5PqP7DQQCFj90MgarBkyVcpH7pVDYzd2pjJxCiClD/T/SRlEC5d2Qkw05nU689tpref1V8wlBeTUk4xANkD179liOFUJwRRnA5Ih//OMfM8e1bNmySdexq2pkV/4rH4dJx5Q69QFdV44mlDMnOhwO9g6hNucT5oVoHXQt2kHRZ8PDwygrK7MkUZuqL0oJeedTqokxNDTE5yKNmBav999/3xLARXk75GydqmEyl8shkUjwMWSfoYCdU6dOIZfL8cIbi8W48hH5gEejUS4pCFi9R4CLRSDo2VJUqRyIR/cka7GyNk0LgqwFq8jn2aXOWXlckWeayunLPLsa+yC/lwOT5IViOiEqp9ul+Am7ZHTFauh2CwK1S11Q1UCiuYLm0DU0NDQWCRa0BF0mk+FVzuv14vz585b0noVA1gbkqD1ZG/693/s9LnBx9OjRSTyWw+Gw5O6wg7zqPvvss7j33nvz0gB27Ve3t6VcqVVXNuoD8ndVdzAzvb6qBa9fvx7nzp2zRN/KXKzsZjYdSIOyKxxM57KD/NzlUmbFQuakPR4Pn5O8iCidgOrhRP2rUgbqueUCIsTJ0/3eeeedOHHiBEcL025Vngu1tbWWvqDsigSi0iiuYcuWLRwOTzs02W9dbps8Nr1eLz9XOf/6dLArnEEg/ls93k5bpt/K841eF+IdInu9kF1hOswVlw5cvPdS7yLzYUEFejAYZF/dpUuXoqOjY1Zbc1mIqIKTApi8Xq8tNyhz6lOdHzBzZ9v5oU8H+d5IQMwGsmsUvVdzt8gTYypf7qkgT0hKtCTztWNjY7ylBexd6PJBtn+omMpwJU92OfVDsRNG7htyG6T2RSIRLj5BSohsVFd5ZDU8X6VHSNDINM25c+f4molEwtK/9Bkl/gLs0y17PB7ceOONADDJAE/3qPaPPD6oXRTnUWxBaDovgaiPYvKmTHVeWdGQuXK6pkx1qN/bjatClZBC2iXfq1y0Z66xoJTLj370I/h8Ph7IxXSm3QBNp9N4+umn2c9b1dyee+65SdqK3I6prkWDqFjBQQJAzuM+W5B3w4kTJzhYRE6KVIrz9/T0oKenh7lgt9vN/dHf3z8pQq4UULVhGbLRkgqfzKZKklwIwuVy4c0338Sbb77JgVf0vRCCNV+HwyyB5/F4eDy5XC7W6t1uN3v/yGMwFovh+eefRzKZ5OyGZDh1OBz41a9+xcU86LNAIGDLE8sVp2hs0vik31I75IyAQlhzkNDnakm3YvtSVSQooEaukFRKUO0EOReR3B90X1M5BNi9nylk7pw4+2g0Om9VvDSHrqGhobFIsKCUy+bNm3lb19XVhVAoNK3vayErqNvt5rzIwWAQqVSKi8QS9UIlxq644oo588CwA7X/Rz/6UcnOSe33+XzI5XKTIvNmC6okBVzcDVB4Nn0/OjpqoavyadYzQSG/T6fTHGYu55ieKWStzeVyca53Sn1ALpmpVIp3QdRGWetLJBK8s5A9UoCLz8Pv92P79u18f5lMBtXV1ezbHo/HcebMGaxcudKyi1Qz/ak7T1XzlPP303/DMCw2BzW/ytjYGH75y1/yOQqB7BqptoOger7IsKM6VCqkEFAuI8D6HACzzwuJkyi1LEilUhgeHrb1hJkLLKhAP3DgAG6//XZ+X15ezmkwZwKVDxNC4ODBgwDMpF9vv/32JD6Qym5R/oy5xlR+1KWCavAkioCET76ycIX4bpPwHhsbw/DwsGXhpeLXcgrRYjHTCSUXAi5Vn46NjbFrZmNjI7xeL4ehU3CLmnhLNsyTO2A+I20mk8Hp06fZTbGzsxODg4OW0Pje3l40NTVZ+kNOW0zXkA3e8v3TsbILL30mxyuo7oTFLMSFUCiq8VV1A1Yh34udi6G6CBBPT+OSxisJ8Xg8bkl5QS6+M8FM5rDsPlqMTCsWCyrQBwcHLX7onZ2daGpqwtmzZy3HTecZoj5w4jkBs3CEHXdFOdWnOx+hVA9kLh4stTeZTHIdSOBi7hZZaymUw1T7nLTf3t5eFt6ysS0Wi1k00WI8TorxMpiqWMdsQHmByI+6o6MDgBnh3N3dPcmoLge8UEAL9Zm6sJL2/u677/I1AHu/7HyxDHROVYirHj9y31DEs1xfU9auyVOmFNGM6pyUFzjaGahZIFVvrJl4ZGWzWUsxazlSFTBjI6qrq4vSwGejtVNE63xhwQS6EAKf//zn+f2RI0fw6KOPwu/389ZT1fTyaZLq4DEMgxN87dmzBxcuXJj0O5psclBIPsxGCM8HnUN0SH19Pfx+P/fb0qVLLa5nPT09lkkDFHZvTqeTi+1SpkY1sCqVShUddg8U10/kfVPqPvb5fLjhhhv49eDgICd7i0Qi2LNnD0fBkkAk7ZrGk6oZk3GSsH79en4G58+ftwhS+Xfq/cpeHIA1s6cqvFWhL4QZsWgXTUqQaZhSCXba4ZCiQYZcO6Evt0v+bjo3RVpEZeOnvIhS/9i5NU/X/qm+m27+0LzId1ypFTxtFNXQ0NBYJFgwDX379u0Wf/C1a9eivb0dbrcbn/mMWfyIcqWrvuWEfG55hmGwltHa2spGM+DidpaKaPziF79gbUz+fam0PpW6oMK7pYLL5UJ9fT0AM8+Ix+NhTSidTsPr9VrqcMoFMKh9hWgJdI5gMMjJvghES1CfBoPBSRpYPsymn3O5HAYGBkq+pV27di0n5wJM2w4lQMtkMti+fTsbDsn4Jmu7NK7lwBk1d0koFMKSJUsAgIugEDKZDM6cOQOfz4dNmzYBmFxEhO5fzrtC1wfMZ0LtoD6mIhvyMZTAi67b3t5ekv6k9rhcLlRVVVnSFsyUu1YNijJk/l+lbWQ6iVwm5XQMhcRLzFYWjIyMzIu7ImFWAl0IcRuAxwE4ATxpGMZfFfrbtWvXIpVK8QPYsmULjhw5glQqhSuvvBKAyQnLif7tuHR1MtHWjQwgdtXn//RP/xT/+I//yJ/JtIRsiS+FUCfvCDLcUc6YUqCyshKBQIATOFHxYhLoAwMDGBgYYHsB2Rfk7W0h/LM8UeSq93Kle4/HY5mwpRbmdgapbDaL4eHhoqJfVZAA93g82Lhxo4WWaGpq4ijOVCqFW265xXItWUDJAUHyWJI55HA4PCn4BLg4Vnbu3IkjR47A5XJZYiPUYCTZj1z2k5ePt6Np5EC0UChkKawsZzedDSgy1u/3o7q62hJ4BkyuDSCD5rTcf/kComRKRaWdZDuPuqhSG6ajXqZTeFTPHdn3HjBz8KjeR3NpIC1aoAshnAC+BWAngE4A+4QQLxuGUVAJHhKidLONjY3sQUFCz+/3Wx6EKtBp8qgaZyKR4KCiK664YlIH/vmf/zkL/KGhITakTAV1sqgDLh9IuNI1SjVhgIsJzuQQfI/HwxWLaMGUtTfVAJUP8j0lEgk2VA8MDDAnSP0xPDxscTklDrnU3LZ6PiroPBvunkB9SOXkZM23traWF7GRkREkEgl+rsTbygY+6mO5iIosfM+fP489e/ZwBkcCLfqvvPIKqqqqMD4+bjFyqoZFABYBL7vl0fiUk3PZPXMqckHnoMyds4VsaM33vcrhy8JQnV9ygi46hgyhgDkGo9GopdIW8fd0juHh4UmR4HKf2Al3O8UuHysggxTE0dHRvBG6c4HZcOjXAGgzDOOUYRgpAN8HcEdpmqWhoaGhMVPMhnJpANAhve8EsF09SAjxMICH1c+feOIJfOUrX2Ff3EgkgsrKSsTjcWzevBnAxXqOcnpYOckRUQeyxm4YBjweDx555BEAwLe+9S3LdR944AE8++yzrE2Sax+1Q9YWZFc02efX7XZbtnfEm9px/VVVVQDASf5LiVgshvHxca5LunXrVgQCAaYPqHgFvR8cHORajeo2UOYiZQ8N6gfa+o+MjHD6WNJ2stksxsbG+NlQaTA5ja+aDlVO5CVfR/UMUUHnpAAf2f99NiBNLx6PY9euXezVEgqFLDlUPB4PGhoa2BZCfUz9SSH48o6O+pPeV1dXY+fOnZZ6uoBZrxQADh48iMHBQdTU1OTNGZNKpSwpKGhnSpD7U362qv+86klG/TBbUFtSqRTOnTuH8vJy3rFQMQp5jMn0CYXq53NjpGPkGITx8XHE43ELdep0Orkd2WwWfX19XPcgGAza2uSKcbeVZQXZk8jmMpcFoe0gir2AEOJuALcZhvGFifcPANhuGMaXp/jNKIATRV1wcSECoH+hG7HA0H1gQveD7gNg+j5YYRhGzXQnmY2Gfh7Acun9sonPpsIJwzC2zeKaiwJCiP2Xez/oPjCh+0H3AVC6PpgNh74PQIsQYqUQwgPgXgAvz7ZBGhoaGhrFoWgN3TCMjBDiywB+BtNt8buGYRyZ5mcaGhoaGnOEWfmhG4bxCoBXZvCTJxITKRsAAAN6SURBVGZzvUUE3Q+6Dwi6H3QfACXqg6KNohoaGhoalxZ0LhcNDQ2NRYJ5E+hCiNuEECeEEG1CiMfm67oLDSHEGSHEYSHEe0KI/ROfVQkhXhNCnJz4X7nQ7Sw1hBDfFUL0CiE+kD6zvW9h4u8nxsYhIcTWhWt56ZCnD74hhDg/MR7eE0LcLn339Yk+OCGEuHVhWl1aCCGWCyF+IYQ4KoQ4IoT46sTnl9tYyNcPpR0Pct6EufqDaTRtB7AKgAfA+wCumI9rL/QfgDMAIspnfw3gsYnXjwH45kK3cw7u+wYAWwF8MN19A7gdwP8DIADsALB3ods/h33wDQD/0+bYKybmhRfAyon54lzoeyhBH9QD2DrxOgzgw4l7vdzGQr5+KOl4mC8NXacJsOIOAE9PvH4awKcXsC1zAsMw3gSgJgbJd993APieYeLXACqEEPXz09K5Q54+yIc7AHzfMIykYRinAbTBnDcfaRiG0WUYxrsTr0cBHIMZZX65jYV8/ZAPRY2H+RLodmkCprqZxQQDwC4hxIGJNAgAUGcYBqVd7AZQZ//TRYd89325jY8vT9AJ35XotkXfB0KIJgBbAOzFZTwWlH4ASjgetFF07vExwzC2AvgkgC8JISzJ1w1zf3XZuRpdrvcN4NsAmgFsBtAF4P8sbHPmB0KIEIAfAvjvhmFE5e8up7Fg0w8lHQ/zJdCLSROwKGAYxvmJ/70AXoK5beqhbeTE/9Ll1L20ke++L5vxYRhGj2EYWcMwcgD+GRe30Yu2D4QQbphC7DnDMF6c+PiyGwt2/VDq8TBfAv2yTBMghAgKIcL0GsAtAD6Aee9UUPXzAH68MC2cd+S775cB/N6Eh8MOACPSdnxRQeGD74Q5HgCzD+4VQniFECsBtAB4Z77bV2oIM6XhvwA4ZhjG/5W+uqzGQr5+KPl4mEcr7+0wLbvtAP73Qlud5+meV8G0VL8P4AjdN4BqALsBnATwcwBVC93WObj3f4e5hUzD5P8eynffMD0avjUxNg4D2LbQ7Z/DPnhm4h4PTUzaeun4/z3RBycAfHKh21+iPvgYTDrlEID3Jv5uvwzHQr5+KOl40JGiGhoaGosE2iiqoaGhsUigBbqGhobGIoEW6BoaGhqLBFqga2hoaCwSaIGuoaGhsUigBbqGhobGIoEW6BoaGhqLBFqga2hoaCwS/P9K4NUrJnPBqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<tigre.utilities.plotimg.plotimg instance at 0x7f4524a174d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tigre.plotimg(np.hstack((imgASDPOCS,imgOSASDPOCS,imgOSSART,imgSARTTV)),slice=32)\n",
    "tigre.plotimg(abs(np.hstack((imgASDPOCS-head,imgOSASDPOCS-head,imgOSSART-head,imgSARTTV-head))),slice=32)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
